Saikosaponin-d protects against liver fibrosis by regulating the estrogen receptor-β/NLRP3 inflammasome pathway
Bibliographic record
Abstract
Liver fibrosis is the most common pathway in most types of chronic liver damage, characterized by an imbalance of ECM degradation and synthesis. Saikosaponin-d (SSd) possesses anti-inflammatory and anti-fibrotic effects. However, the underlying mechanism by which SSd represses hepatic stellate cell (HSC) activation remains unclear. Here, we found that SSd remarkably alleviated carbon tetrachloride (CCl4)-induced liver fibrosis, as evidenced by decreased collagen levels and profibrotic marker (COl1a1 and α-smooth muscle actin (SMA)) expression. SSd repressed CCl4-induced NOD-like receptor family pyrin-domain-containing-3 (NLRP3) activation in fibrotic livers, as suggested by decreased levels of NLRP3, IL-18, and IL-β. The primary HSCs of CCl4 mice exhibited a significant increase in profibrotic marker expression and NLRP3 activation, but SSd treatment reversed this effect. SSd also repressed TGF-β-induced profibrotic marker expression and NLRP3 activation in vitro. Mechanistically, TGF-β decreased the expression of estrogen receptor-β (ERβ) in HSCs, whereas SSd treatment reversed this effect. ERβ inhibition enhances NLRP3 activation in HSCs. More importantly, ERβ or NLRP3 inhibition partially destroyed the function of SSd in liver fibrosis. In summary, the current data suggest that SSd prevents hepatic fibrosis by regulating the ERβ/NLRP3 inflammasome pathway and suggests SSd as a potential agent for treating liver fibrosis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".